[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124611-en":3,"doc-seo-124611-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124611,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Management and Evaluation of the Performance of end-to-end 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment","The 3GPP defines network slicing as scalable resources that match user requirements, enabling efficient management through the combination of machine learning and slicing. Resource sharing across multiple operators—covering towers, spectrum, and infrastructure—reduces 5G deployment costs. The proposed prototype connects end users to multiple inter- and intra-slices based on demand, implements slice sets via softwarization and virtualization, then generates real-time traffic under varied scenarios for end-to-end analysis. Extracted flow-based features are used to train and select machine learning models in MATLAB, minimizing CPU and training time, while regression predicts slice type with minimum squared error to support sustainable future networks.","Management and Evaluation of the Performance of end-to-end 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment  \nNoor Abdalkarem Mohammedali, Student Member, IEEE, Triantafyllos Kanakis, Member, IEEE, Ali Al-Sherbaz, Member, IEEE, and Michael Opoku Agyeman, Senior Member, IEEE  \nAbstract—The 3G Partnership Project (3GPP) defined network slicing as a set of resources that could be scaled up and down to cover users’ requirements. Machine learning and network slicing will be used together to manage and optimize resources efficiently. Sharing resources across multiple operators, such as towers, spectrum and infrastructure, can reduce the cost of 5G resources. In the proposed prototype, the end-user is connected to more than eight inter and intra-slices according to the demands. A set of slices is implemented over the 5G networks to provide an efficient service to the end-user using softwarization and virtualization technologies. Traffic is generated over multiple scenarios then End-to-End slicing traffic was analyzed after generating realtime traffic over the 5G networks. Also, all the features extracted from the traffic based on the flow behaviours and a set of elements selected from the datasets according to machine learning behaviours. Multiple machine learning algorithms are applied to our datasets using MATLAB classification application. After that, the best model is chosen to train and predict the slices using less CPU and training time to reduce the computational power in future networks and build a sustainable environment. Furthermore, the regression application predicts the slice type on the third dataset with the minimum squared error.  \nIndex Terms—5G, NFV, Network Slicing, Future Network, Inter-Slice, Machine Learning, Network Services, Intra-Slice, Resources Allocation, E2E.  \nI. INTRODUCTION  \nEND-to-End slicing is a new technology that promises  \nto provide flexibility, more sustainability, better performance and lower costs in mobile networks. Network slicing enables operators to create multiple virtual networks on a single physical network, allowing for more flexibility and customizability using Software-Defined Networks (SDN) and Network Function Virtualization (NFV) . In [1], the authors reviewed all the slicing issues and focused on employing areal-time management algorithm to regulate and manage the virtual network’s resource distribution. In addition, network  \nManuscript received December 6, 2022; revised March 2, 2023 . Date of publication March 24, 2023 . Date of current version March 24, 2023 . The associate editor prof. Dinko Begusic has been coordinating the review of this manuscript and approved it for publication.  \nThe paper was presented in part at the International Conference on Software, Telecommunications and Computer Networks (SoftCOM) 2022 .  \nN. Mohammedali, T. Kanakis and M. Agyeman are with the Computing Department, University of Northampton, Northampton, NN1 5PH, UK, e-mails: noor.mohammedali, Triantafyllos.Kanakis, [Michael.OpokuAgyeman@northampton.ac.uk](Michael.OpokuAgyeman@northampton.ac.uk).  \nA. Al-Sherbaz is with the University of Gloucestershire, GL50 2RH, UK. Digital Object Identifier (DOI): 10.24138/jcomss-2022-0163  \nslicing benefits are highlighted with their advantages in future networks. The 5G End-to-End slicing (5GE2ES) also supports using unlicensed spectrum, which could help reduce costs and improve efficiency [2] . The 5GE2ES significantly improves the performance of mobile networks by reducing latency, jitter, and packet loss. It also enables sharing resources among different types of traffic, resulting in more efficient use of network resources [3] . The main idea of using the NFV in future telecommunications networks is to optimize the functions and services built for future networks. While using the SDN is used to optimize the fundamental system [4] . In future networks, all functions will be implemented and reconfigured on top of the virtual ne","cbCaiokTg60XvKHY","https://ap.wps.com/l/cbCaiokTg60XvKHY","pdf",3579644,1,12,"English","en",105,"# Introduction\n## End-to-End slicing overview\n## Network slicing foundations (3GPP, SDN, NFV)\n## Motivation and related work","[{\"question\":\"How does the proposed approach connect end users to inter- and intra-slices?\",\"answer\":\"The prototype connects the end user to more than eight inter and intra-slices based on the demands. A set of slices is implemented over the 5G networks using softwarization and virtualization technologies.\"},{\"question\":\"What data and analysis method are used to evaluate end-to-end slicing performance?\",\"answer\":\"Traffic is generated across multiple scenarios, then end-to-end slicing traffic is analyzed. Features are extracted from traffic based on flow behaviors and selected elements from datasets to support machine learning.\"},{\"question\":\"Which machine learning models are applied and how is the best model selected?\",\"answer\":\"Multiple machine learning algorithms are applied to the datasets using MATLAB classification. The best model is chosen to train and predict slices with reduced CPU usage and training time, and regression is used to predict slice type with minimum squared error.\"}]","Management and Evaluation of the Performance of end-to-end 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment | PDF",1785893310,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"management-and-evaluation-of-the-performance-of-end-to-end-5g-interintra-slicing-using-machine-learning-in-a-sustainable-environment","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/management-and-evaluation-of-the-performance-of-end-to-end-5g-interintra-slicing-using-machine-learning-in-a-sustainable-environment/124611/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the proposed approach connect end users to inter- and intra-slices?","Question",{"text":75,"@type":76},"The prototype connects the end user to more than eight inter and intra-slices based on the demands. A set of slices is implemented over the 5G networks using softwarization and virtualization technologies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and analysis method are used to evaluate end-to-end slicing performance?",{"text":80,"@type":76},"Traffic is generated across multiple scenarios, then end-to-end slicing traffic is analyzed. Features are extracted from traffic based on flow behaviors and selected elements from datasets to support machine learning.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are applied and how is the best model selected?",{"text":84,"@type":76},"Multiple machine learning algorithms are applied to the datasets using MATLAB classification. The best model is chosen to train and predict slices with reduced CPU usage and training time, and regression is used to predict slice type with minimum squared error.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]